
R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

[Previously saved workspace restored]

> ### Jonathan D. Klingler, Gary E. Hollibaugh, Jr., and Adam J. Ramey
> ### "Don't Know What You Got: A Bayesian Hierarchical Model of Neuroticism and Ideological Uncertainty
> ### Political Science Research and Methods
> ###
> ###
> ### Hierarchical Model Estimation Code
> ### Description: This file (one of eight) sets up the hierarchical model on a cluster node.
> ###
> ### Note 1: Run the Preprocessing_and_Summary.R file beforehand to generate the CCES2014.Rda file.
> ### Note 2: This file ends in SingleCoreXXX.R, and generates a file called DKWYGCoreXXX.Rda, where XXX is a number from 1-8.
> ### Note 3: Make sure the directories are set up properly and that all files are in this file's directory.
> ### Note 4: For ease of use, template script files called DKWYGCoreXXX.sh are included in the "Cluster Scripts" directory.
> ### Note 5: This file will start a process that will take several days to complete, so a cluster is recommended.
>
>
> rm(list=ls())
> ptm <- proc.time()
> 
> library(foreign)
> library(rjags)
Loading required package: coda
Linked to JAGS 3.4.0
Loaded modules: basemod,bugs
> library(grDevices)
> library(MASS)
> library(random)
> 
> 
> 
> load("CCES2014.Rda")
> 
> 
> # need to remove datapoints where questions were not asked
> # and where personality not elicited
> cces <- cces[!is.na(cces$self_emoti) & !is.na(cces$self_consc) & !is.na(cces$self_agree) 
+              & !is.na(cces$self_extra) & !is.na(cces$self_openn) & !is.na(cces$CC421a) 
+              & !is.na(cces$newsint) 
+              & !(cces$CC334C == "Not Asked") 
+              & !(cces$CC334D == "Not Asked") 
+              & !(cces$CC334E == "Not Asked") 
+              & !(cces$CC334F == "Not Asked") 
+              & !(cces$CC334G == "Not Asked") 
+              & !(cces$CC334K == "Not Asked") 
+              & !(cces$CC334L == "Not Asked") 
+              & !(cces$CC334M == "Not Asked") 
+              & !(cces$CC334W == "Not Asked"),]
> 
> attach(cces)
> 
> ##the dependent variable
> obama <- CC334C
> clinton <- CC334D
> cruz <- CC334E
> paul <- CC334F
> bush <- CC334G
> dem <- CC334K
> rep <- CC334L
> teaparty <- CC334M 
> scotus <- CC334W
> 
> y <- cbind(obama,cruz,clinton,paul,bush,dem,rep,teaparty,scotus)
> for (i in 1:ncol(y)) y[,i] <- as.numeric(y[,i])
> 
> y[y>=8] <- 0
> y[is.na(y)] <- 0
> y <- y+1
> 
> ##covariates for decisiveness and the saliency intercepts
> self_neuro <- 8 - self_emoti
> self_openn <- (self_openn - 1)/6
> self_consc <- (self_consc - 1)/6
> self_extra <- (self_extra - 1)/6
> self_agree <- (self_agree - 1)/6
> self_neuro <- (self_neuro - 1)/6
> polinterest <- (newsint=="Most of the time")*1
> unkinterest <- (newsint=="Don't know")*1
> faminc_recode <- faminc
> faminc_recode[which(faminc_recode == "$150,000 - $199,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$200,000 - $249,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$250,000 - $349,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$350,000 - $499,999")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$500,000 or more")] <- levels(faminc_recode)[17]
> faminc_recode[which(faminc_recode == "$250,000 or more ")] <- levels(faminc_recode)[17]
> income <- factor(faminc_recode)
> income_notsay <- (faminc_recode == "Prefer not to say")*1
> age <- 2014 - birthyr
> age2 <- (age^2)/100
> black <- (race == "Black")*1
> hisp <- (race == "Hispanic")*1
> other <- (race == "Asian" | race == "Native American" | race == "Mixed" | race == "Other" | race == "Middle Eastern")*1
> fulltime <- (employ == "Full-time")*1
> parttime <- (employ == "Part-time")*1
> unemploy <- (employ == "Unemployed")*1
> retired <- (employ == "Retired")*1
> female <- I(gender == "Female")*1
> education <- as.numeric(educ)
> 
> X <- cbind(1,
+            self_openn,
+            self_consc,
+            self_extra,
+            self_agree,
+            self_neuro,
+            female,
+            age,
+            age2,
+            black,
+            hisp,
+            other,
+            education,
+            polinterest,
+            unkinterest,
+            income,
+            income_notsay,
+            fulltime,
+            parttime,
+            unemploy,
+            retired)
> 
> ##covariate for saliency regression (equal to 1 if respondent and stimulus are the same party)
> Xs <- cbind((CC421a=="Democrat")*1, (CC421a=="Republican")*1, 
+             (CC421a=="Democrat")*1, (CC421a=="Republican")*1,
+             (CC421a=="Republican")*1, (CC421a=="Democrat")*1,
+             (CC421a=="Republican")*1, (CC421a=="Republican")*1,
+             (CC421a=="Republican")*1)
> 
> forJags <- list(y=y, x=X, Xs=Xs)
> 
> forJags$J <- nrow(y)
> forJags$K <- ncol(y)
> forJags$R <- ncol(X)
> forJags$nCat <- length(table(y))
> 
> ## priors
> forJags$b0 <- rep(0,ncol(X))
> forJags$B0 <- diag(.25,ncol(X))
> 
> ## initial values
> inits <- function(){list(beta_e=rep(0,ncol(X)),
+                    beta_d=rep(0,ncol(X)),
+                    beta_a=rep(0,ncol(X)),
+                    beta_s=0,
+                    sig_e=1,
+                    sig_a=1,
+                    tau0=seq(1,2,length=(forJags$nCat-3)))}
> 
> 
> n.tune <- 10000
> n.burnin <- 40000
> n.saved <- 50000
> n.thin <- 1
> 
> 
> set.seed(1)
> temp.model <- jags.model(file="MLV2.bug",
+                          inits=inits,
+                          data=forJags,
+                          n.chains = 1,
+                          n.adapt = n.tune)
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
   Graph Size: 277330

Initializing model

> update(temp.model, n.iter = n.burnin)
> dkwyg.1 <- coda.samples(temp.model,
+                         variable.names=c("beta_s","tau","beta_d","beta_e",
+                                          "beta_a","beta_b","gamma"),
+                         n.iter = n.saved,
+                         thin = n.thin)
> 
> save(dkwyg.1, file="DKWYGCore1.Rda")
> detach(cces)
> proc.time() - ptm
      user     system    elapsed 
298828.651      2.003 298894.357 
> 
> 
> proc.time()
      user     system    elapsed 
298838.889      2.614 298905.223 
